ArticleJAMA network open2020
Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia.
Article in JAMA network open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Application of artificial intelligence and psychosocial functioning in psychosis: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- Optimizing opioid use disorder treatment with naltrexone or buprenorphine.Drug and alcohol dependence · 2021Trial
- Development and validation of a precision treatment rules for first-line antipsychotic recommendations in first episode psychosis jointly incorporating effectiveness, side effects and patient preferences.Translational psychiatry · 2026Article
- Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review.Translational psychiatry · 2026Article
- Data driven versus standard practice policies for personalized antidepressant treatment: a retrospective cohort study of electronic health records.BMC psychiatry · 2026Article
- Ensuring generalizability and clinical utility in mental health care applications: Robust artificial intelligence-based treatment predictions in diverse psychosis populations.Psychiatry and clinical neurosciences · 2026Article
- Decoding psychotropic-induced metabolic disturbances: gut-brain axis, multi-omics, and nutrition-lifestyle integration.Frontiers in nutrition · 2026Review
- Early metoprolol use in ICU patients with congestive heart failure is associated with increased 30-day mortality: a causal machine learning study.Frontiers in pharmacology · 2026Article
- Developing and validating a machine learning pharmaceutical therapy recommender system for US-based hospital in-patients with schizophrenia spectrum disorders.BMC psychiatry · 2025Article
- Personalized Prediction of Clozapine Treatment Response Using Therapeutic Drug Monitoring Data in Japanese Patients with Treatment-Resistant Schizophrenia.Journal of clinical medicine · 2025Article
- Advancing treatment response prediction in first-episode psychosis: integrating clinical and electroencephalography features.Psychiatry and clinical neurosciences · 2025Article
- Can artificial intelligence be the future solution to the enormous challenges and suffering caused by Schizophrenia?Schizophrenia (Heidelberg, Germany) · 2025Review
- Biomarker discovery using machine learning in the psychosis spectrum.Biomarkers in neuropsychiatry · 2024Article
- Multivariable prediction of functional outcome after first-episode psychosis: a crossover validation approach in EUFEST and PSYSCAN.Schizophrenia (Heidelberg, Germany) · 2024Article
- Prediction of Clinical Outcomes in Psychotic Disorders Using Artificial Intelligence Methods: A Scoping Review.Brain sciences · 2024Article
- Assessing treatment switch among patients with multiple sclerosis: A machine learning approach.Exploratory research in clinical and social pharmacy · 2023Article
- Optimally Choosing Medication Type for Patients With Opioid Use Disorder.American journal of epidemiology · 2023Article
- Tyler VanderWeele's contribution to the Discussion of 'Experimental evaluation of algorithm-assisted human decision-making: application to pretrial public safety assessment' by Imai et al.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2023Article
- Machine learning methods to predict outcomes of pharmacological treatment in psychosis.Translational psychiatry · 2023Review
- Comparison of prediction methods for treatment continuation of antipsychotics in children and adolescents with schizophrenia.Evidence-based mental health · 2022Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
Importance: Little guidance exists to date on how to select antipsychotic medications for patients with first-episode schizophrenia. Objective: To develop a preliminary individualized treatment rule (ITR) for patients with first-episode schizophrenia. Design, Setting, and Participants: This prognostic study obtained data from Taiwan's National Health Insurance Research Database on patients with prescribed antipsychotic medications, ambulatory claims, or discharge diagnoses of a schizophrenic disorder between January 1, 2005, and December 31, 2011. An ITR was developed by applying a targeted minimum loss-based ensemble machine learning method to predict treatment success from baseline clinical and demographic data in a 70% training sample. The model was validated in the remaining 30% of the sample. The probability of treatment success was estimated for each medication for each patient under the model. The analysis was conducted between July 16, 2018, and July 15, 2019. Exposures: Fifteen different antipsychotic medications. Main Outcomes and Measures: Treatment success was defined as not switching medication and not being hospitalized for 12 months. Results: Among the 32 277 patients in the analysis, the mean (SD) age was 36.7 (14.3) years, and 15 752 (48.8%) were male. In the validation sample, the treatment success rate (SE) was 51.7% (1.0%) under the ITR and was 44.5% (0.5%) in the observed population (Z = 7.1; P < .001). The estimated treatment success if all patients were given a prescription for 1 medication was significantly lower for each of the 13 medications than under the ITR (Z = 4.2-16.8; all P < .001). Aripiprazole (3088 [31.9%]) and amisulpride (2920 [30.2%]) were the medications most often recommended by the ITR. Only 1054 patients (10.9%) received ITR-recommended medications. Observed treatment success, although lower than the success under the ITR, was nonetheless significantly higher than if medications had been randomized (44.5% [SE, 0.55%] vs 41.3% [SE, 0.4%]; Z = 6.9; P < .001), although only marginally higher than if medications had been randomized in their observed population proportions (44.5% [SE, 0.5%] vs 43.5% [SE, 0.4%]; Z = 2.2; P = .03]). Conclusions and Relevance: These results suggest that an ITR may be associatded with an increase in the treatment success rate among patients with first-episode schizophrenia, but experimental evaluation is needed to confirm this possibility. If confirmed, model refinement that investigates biomarkers, clinical observations, and patient reports as additional predictors in iterative pragmatic trials would be needed before clinical implementation.
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